{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.6.2_Support Vector Machine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,roc_auc_score,roc_curve\n",
    "\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating toy dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.707018</td>\n",
       "      <td>3.407676</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.649701</td>\n",
       "      <td>2.818427</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.597445</td>\n",
       "      <td>2.494933</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.557623</td>\n",
       "      <td>2.569730</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.311609</td>\n",
       "      <td>0.670486</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         X1        X2  y\n",
       "0  0.707018  3.407676  0\n",
       "1  3.649701  2.818427  0\n",
       "2  3.597445  2.494933  0\n",
       "3  1.557623  2.569730  0\n",
       "4  1.311609  0.670486  0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#setting the seed \n",
    "X = np.random.normal(size = (200,2))\n",
    "X[:100,] += 2\n",
    "X[100:150,] -= 2\n",
    "y = [0]*150 + [1]*50\n",
    "\n",
    "data = pd.DataFrame({'X1':X[:,0],'X2':X[:,1],'y':y})\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250f83a9080>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the data \n",
    "plt.figure(figsize = (10,6))\n",
    "sns.scatterplot(x = 'X1',y = 'X2',hue = 'y',data = data,s = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can say that there is no possible linearly separation between the two classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 2) (100, 2)\n"
     ]
    }
   ],
   "source": [
    "#splitting the data into train and test\n",
    "X_train,X_test,y_train,y_test = train_test_split(data.drop('y',axis=1),data['y'],test_size = 0.5,random_state = 1)\n",
    "print(X_train.shape,X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting the model with radical kernal to training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma=1, kernel='rbf', max_iter=-1,\n",
       "    probability=False, random_state=None, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC(kernel='rbf',gamma = 1,C = 1)\n",
    "svm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the decision boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'X2')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "plot_decision_regions(np.array(X_train), np.array(y_train), clf=svm, legend=2)\n",
    "\n",
    "plt.xlabel('X1')\n",
    "plt.ylabel('X2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Support Vectors are  54\n",
      "Accuracy on training data is  0.94\n"
     ]
    }
   ],
   "source": [
    "print('Number of Support Vectors are ',svm.support_vectors_.shape[0])\n",
    "print('Accuracy on training data is ',svm.score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing a high number of C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Support Vectors are  18\n",
      "Accuracy on training data is  1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm = SVC(kernel='rbf',gamma = 1,C = 10e5)\n",
    "svm.fit(X_train,y_train)\n",
    "\n",
    "### Plotting the decision boundary\n",
    "\n",
    "plt.figure(figsize = (10,6))\n",
    "plot_decision_regions(np.array(X_train), np.array(y_train), clf=svm, legend=2)\n",
    "\n",
    "plt.xlabel('X1')\n",
    "plt.ylabel('X2')\n",
    "\n",
    "print('Number of Support Vectors are ',svm.support_vectors_.shape[0])\n",
    "print('Accuracy on training data is ',svm.score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Altough we are having a better training accuracy this time,but we can see that the model is overfitting the data, and it will perform poor on test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing the optimal value of C and gamma using cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
       "                         'gamma': [0.5, 1, 2, 3, 4]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_dict = {'C':[0.1,1,10,100,1000],'gamma':[0.5,1,2,3,4]}\n",
    "search = GridSearchCV(SVC(kernel = 'rbf'),params_dict,cv =10)\n",
    "search.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'gamma': 0.5}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the values are different as compared to the one that we have in the book....and i hope that you know why it is happening that way..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance of best_model on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy is  0.84\n",
      "Confusion matrix is - \n",
      "[[63  7]\n",
      " [ 9 21]]\n"
     ]
    }
   ],
   "source": [
    "test_preds = best_model.predict(X_test)\n",
    "print('Test accuracy is ',accuracy_score(y_test,test_preds))\n",
    "print('Confusion matrix is - ')\n",
    "print(confusion_matrix(y_test,test_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.6.3 ROC curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc(cls,X,y,title):\n",
    "    probs = cls.predict_proba(X)[:,1]\n",
    "    fpr, tpr, _ = roc_curve(y,probs)\n",
    "    auc = roc_auc_score(y,probs)\n",
    "    \n",
    "    plt.figure(figsize = (6,6))\n",
    "    plt.plot(fpr,tpr,label=\"auc=\"+str(auc)[0:4],c = 'r')\n",
    "    plt.plot([0,1],[0,1],alpha = 0.1,c = 'b')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title(title)\n",
    "    plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma=0.5, kernel='rbf',\n",
       "    max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#because we need probability = True to get probs\n",
    "best_model.probability = True\n",
    "best_model.gamma = 0.5\n",
    "best_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(best_model,X_train,y_train,'training roc, gamma = 0.5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#roc curve if gamma is high\n",
    "best_model.probability = True\n",
    "best_model.gamma = 50\n",
    "best_model.fit(X_train,y_train)\n",
    "\n",
    "plot_roc(best_model,X_train,y_train,'training roc, gamma = 0.5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "woah...but wait, its training roc, not test roc :("
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the same on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_roc_terms(cls,X,y):\n",
    "    probs = cls.predict_proba(X)[:,1]\n",
    "    fpr,tpr,_ = roc_curve(y,probs)\n",
    "    auc = roc_auc_score(y,probs)\n",
    "    \n",
    "    return fpr,tpr,auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc_test(cls,X_train,y_train,X_test,y_test):\n",
    "    fpr,tpr,auc = find_roc_terms(cls,X_train,y_train)\n",
    "    fpr_test,tpr_test,auc_test = find_roc_terms(cls,X_test,y_test)\n",
    "    \n",
    "    fig,(ax1,ax2) = plt.subplots(1,2,figsize = (12,6))\n",
    "    ax1.plot(fpr,tpr,label=\"auc=\"+str(auc)[0:4],c = 'r')\n",
    "    ax1.plot([0,1],[0,1],alpha = 0.1,c = 'b')\n",
    "    ax1.set_xlabel('False Positive Rate')\n",
    "    ax1.set_ylabel('True Positive Rate')\n",
    "    ax1.set_title('Train Roc')\n",
    "    ax1.legend()\n",
    "    \n",
    "    ax2.plot(fpr_test,tpr_test,label=\"auc=\"+str(auc_test)[0:4],c = 'r')\n",
    "    ax2.plot([0,1],[0,1],alpha = 0.1,c = 'b')\n",
    "    ax2.set_xlabel('False Positive Rate')\n",
    "    ax2.set_ylabel('True Positive Rate')\n",
    "    ax2.set_title('Test Roc')\n",
    "    ax2.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iND4utXLH+44I0H19fTr++OOTLgNAG6tOARYKrVk/183o2cAmtHJ3wMlJ6bzzmv8+WzQ9HRtcjY5Kwy3eGrYjAjQAbIV7TAFW188xGw4glTpgd8BGmZmRZmcjOFc2DG2pjlgDDQBbsXwKcHAw6WoAAOuZm4sAPTQUfTsJBGgAXW1qKrbpHhtr/RQgAKA+CwvRtwcG4qLBpLCEA/Vp9kUMbXThAtrf9HSMZIyMxAMAkF6Li7HcLpuN5XZJYgQa9alexNAsbXLhAtrf7Gw8hoZi9BkAkF5LSxGe+/vjQm+zZOthBBr14yIGtLn5+Rh9HhxMdgoQALCxQiHukpTJpCM8S4xAA+gyCwtx0WA2S3gGgLQrFuP+/D09cX/+npQk15SUAQDNt7QU4TktU4AAgLWVShGepQjPmUyy9SzHEg4AXSGfjynA3l7CM4A2sPKi/S67yL66M6y7tGNH9O40YQQaQMdbvn4uTVOAALCmlRftd9FF9tXwXCrFgEfawrPECDSADlddP2dGeAbQZrrwon33GPAoFiM89/cnXdHq+FECoGOlef0cAOCRquE5n4/7PGezSVe0NgI0gI5UnQIslyM8p3EKEABw0IEDcbH3xETsNJhmBGgAHWf5+rnt26W+vqQrAgCsJ5eLnQbHx2ODq7QjQAPoKNUpwEIhpgDTun4OABCmp2ODq9FRaXg46WpqQ4AG0DHcYwqwun4u7VOAANDtZmak2dkIzqOjSVdTOwI0gI6xfApwcDDpagAA65mbiwA9NBR9u50QoAF0hKmp2KZ7bKx9pgABoFstLETfHhiIiwbbTfddl75yZx/Up8t2QkJ7mJ6OkYyRkXgAQNM1O0908M/bxcVYbpfNxnK7dtR9I9Ard/ZBfbpoJyS0h9nZeAwNxegzALREs/NEh/68XVqK8NzXFxulmCVd0eZ03wi01JU7+wCdaH4+Rp8HB9tzChBAmyNP1KVQiLskZTJxi9F2Dc9SN45AA+gICwtx0WA2S3gGgLQrFuP+/D09EZ572jyBtnn5ALrR0lKE5/7+9p4CBIBuUCpFeJYiPGcyydbTCARoAG0ln48pwN5ewjMApF11Z1j3CM+9HbJ4mAANoG2sXD/X7lOAANDJquG5VIoBj76+pCtqHH78AGgL1fVzZoRnAEg79xjwKBYjPPf3J11RY/EjCEDqdeL6OQDoVNXwnM/HfZ6z2aQrajwCNIBUq04BlsudtX4OADpVLhcXe09MxE6DnYgfRQBSy/3g+rnt2ztr/RyAlNtop8EO3ilwK3K5uM3o+HhscNWpGIEGkErV8FwoxBRgp62fA5ByG+002KE7BW7F9HRscDU6Kg0PJ11NczECDSB13GOr1+r6uU6dAgSQcuw0WLOZGWl2NoLz6GjS1TQfI9AAUieXkxYXYwpwcDDpagAA65mbiwA9OBh9uxsQoAGkytRUrJ8bG+v8KUAAaHcLC9G3BwbiosFuQYAGkBozMzGSMTISDwBAei0uxnK7bDaW23XTzrAEaACpMDsbAXpoKEafAQDptbQU4bmvLzZK6abwLBGgAaTA/HxcvT042F1TgADQjgqF2Cglk4lbjHZbeJYI0AAStrAQFw1ms4RnAEi7YjFuMdrTE+G5p0uTZJeeNoA0WFqK8Nzf351TgADQTkqlCM9ShOdMJtl6ksR9oAEkIp+PKcDeXsIz8LCNdr9D67DT4COUyxGe3SM893Z5gmQEGkDLrVw/161TgMAhNtr9Dq3DToMPq4bnUikGPPr6kq4oeV3+/wcArVZdP2dGeAZWxe53SBH3GPAoFiM89/cnXVE68KMLQMuwfg4A2kc1POfzcZ/nbDbpitKDAA2gJapTgOUy6+cAoB3kcnGx98RE7DSIgwjQAJrO/eD6ue3bWT8HAGmXy8VtRsfGYoMrPBIBGkBTVcNzoRBTgKyfA4B0m56ODa5GR6WRkaSrSaemBmgzO8PM7jCzO83s4lW+fqyZXWdm3zWzW8zsRc2sB0BrucdWr9X1c0wBphs9G8DsbDyGhyNAY3VNC9BmlpH0AUlnSjpR0ivN7MQVh71V0mfc/cmSXiHpL5tVD4DWy+WkxUVpfDy26UZ60bMBzM3F6PPgYPRtrK2ZI9BPk3Snu//I3fOSPiXpnBXHuKSxysfjkn7SxHoAtNDU1MH1c8PDSVeDGtCzgS62sBB9e2AgLhrE+pp5HfxRku5Z9ny/pKevOOYdkv7ZzF4vaVjSC5pYD4AWmZmJkYyREdbPtRF6NtClFhdjuV02G8vt2Bl2Y80cgV7tt99XPH+lpI+4+9GSXiTpY2Z2SE1mdoGZ7TWzvffff38TSgXQKLOzEaCHhmL0GW2Dng10oXw+wnNfX2yUQniuTTMD9H5Jxyx7frQOne57raTPSJK7f1PSgKQdK1/I3Xe5+05333n44Yc3qVwAWzU/f3D9HFOAbYeeDXSZQiHukpTJxC1GCc+1a2aAvknSCWZ2vJn1Ky44uWbFMf8p6TRJMrMnKJoxwxVAG1pYiIsGs1nCc5uiZwNdpFiM8NzTE+G5hxsb16Vpv13uXpR0oaQvS7pdceX2bWZ2qZmdXTnsTZJ+w8y+J+mTks5395VThgBSbmkpwnN/P1OA7YqeDXSPUinCsxThOZNJtp521NTNdN39WknXrvjc25d9/H1Jz25mDQCaK5+XHnootuYmPLc3ejbQ+crlCM/uEZ57m5oEOxcD9gA2rVCI8FxdP8cUIACkVzU8l0ox4NHXl3RF7YsfdwA2pbp+zozwDABp5x4DHsVihOf+/qQram/8yANQN9bPAUD7qIbnfD7u85zNJl1R+yNAA6hLdQqwXGb9HAC0g1wuLvaemIidBrF1/OgDUDP3g+vntm9n/RxQt127pN271/76vn3S5GTr6kHHy+XiNqNjY7HBFRqDEWgANamG50IhpgBZPwdswu7dEZLXMjkpnXde6+pBR5uejg2uRkelkZGkq+ksjEAD2JB7bPVaXT/HFCCwBZOT0p49SVeBDjc7G4/h4QjQaCxGoAFsKJeTFhel8fHYphsAkF5zczH6PDgYfRuNR4AGsK6pqYPr54aHk64GALCehYXo2wMDcdEgmoMADWBNMzMxkjEywvo5AEi7xcVYbpfNxnI7doZtHgI0gFXNzkaAHhqK0WcAQHrl8xGe+/pioxTCc3MRoAEcYn7+4Po5pgABIN0KhbhLUiYTtxglPDcfARrAIywsxEWD2SzhGQDSrliM8NzTE+G5h2TXEvw2A3jY0lKE5/5+pgABIO1KpQjPUoTnTCbZeroJ94EGICnWzz30UGzNTXhGV9lod8BGYqdBNEi5HOHZPcJzL4mupRiBBqBCIcJzdf0cU4DoKhvtDthI7DSIBqiG51IpBjz6+pKuqPvw/xWgy1XXz5kRntHF2B0QbcI9BjyKxQjP/f1JV9Sd+FEJdDHWzwFA+6iG53w+7vOczSZdUfciQANdqjoFWC6zfg4A2kEuFxd7T0zEToNIDgEa6ELuB9fPbd/O+jkASLtcLm4zOjYWG1whWQRooMtUpwALhZgCZP0cAKTb9HRscDU6Ko2MJF0NJAI00FXcY6vXpaUIz0wBAkC6zc7GY3g4AjTSgQANdJFcTlpclMbHY5tuAEB6zc3F6PPgYPRtpAeXDQFdYmoq1s+NjsZIBtA1Ntoohc1NkEILC9G3BwbiokGkCyPQQBeYmYmRjJERpgDRhTbaKIXNTZAyi4ux3K6/P5bbsTNs+jACDXS42dkI0ENDcfU20JXYKAVtIp+P8NzXF3dJIjynEyPQQAebnz+4fo4pQABIt0IhbjGayRCe044ADXSoxcW4aDCbJTwDQNoVixGee3oiPPeQ0FKNPx6gAy0tHVw/t20boxgAkGalUoRnKcJzJpNsPdgYARroMPl8bJTS20t4BoC0K5cjPLtHeO7l6rS2QIAGOkihEOG5un6OKUAASK9qeC6VYsCjry/pilArfrwCHaK6fs6M8AwAaeceAx7FYoTn/v6kK0I9+BELdADWzwFA+6iG53w+LvLOZpOuCPVipQ3Q5qpTgOWytGMH6+eAQ3YeZKdBpEwuFxd7T0zEbUbRfhiBBtqYO+vngEOs3HmQnQaRIrlcbNM9NhYbXKE9MVYFtKnqFGChEOGZKUBgGXYeRApNT8cGV6Oj0shI0tVgKxiBBtqQe9zneWlJOuwwaWAg6YoAAOuZnY3H8HAEaLQ3AjTQhnK52GlwfJz1cwCQdnNzMfo8OBh9G+2PAA20mampWD83OhojGQCA9FpYiL49MBAXDaIzEKCBNjIzEyMZIyNMAQJA2i0uxnK7/v5YbsfOsJ2DAA20idnZCNBDQ3H1NgAgvfL5CM99fXF/fsJzZ6k5QJsZk8VAQubnD66fYwoQtaBnA8kpFOIWo5kM4blTbRigzexZZvZ9SbdXnp9iZn/Z9MoASIopwFwublNHeMZG6NlAsorFCM89PRGee5jr70i1/LG+V9L/kPSgJLn79yT9UjOLAhCWlg6un9u2jVEM1ISeDSSkVIrwLEV4zmSSrQfNU9P/i9z9nhWfKjWhFgDL5POxUUpvL+EZ9aFnA61XLkd4do/w3MtWdR2tlj/ee8zsWZLczPolvUGVqUEAzVEoRHiurp9jChB1oGcDLVYNz6VS9Oy+vqQrQrPV8mP5tyT9jqSjJO2XNCnpt5tZFNDNquvnzAjP2BR6NtBC7jHgUSzGbGF/f9IVoRVqGYF+vLu/avknzOzZkr7RnJKA7sX6OTQAPRtokWp4zufjPs/ZbNIVoVVqGdv6ixo/B2ALqlOA5TLr57Al9GygRXK5uNh7YiJuM4ruseaPaDN7pqRnSTrczN647EtjkhgXAxrI/eD6uW3bWD+H+tGzgdbK5WKb7rGx2OAK3WW9Ma5+SSOVY5ZvGjwt6dxmFgV0k+oUYKEQ4ZkpQGwSPRtokenp2OBqZCQe6D5rBmh3/5qkr5nZR9z97s28uJmdIel9itGPv3H3d61yzMskvUOSS/qeu5+3mfcC2tWBAzEFeNhh0sBA0tWgXdGzgdaYnY3H8HCMPqM71bLKct7MLpP0REkP/3h39+ev901mlpH0AUmnK64Ev8nMrnH37y875gRJb5H0bHc/YGaP2sQ5AG3rwIHYaXB8nPVzaBh6NtAkc3Mx+jw4GH0b3auWiwg/IekHko6X9IeS7pJ0Uw3f9zRJd7r7j9w9L+lTks5ZccxvSPqAux+QJHe/r8a6gbY3NRXr50ZHYyQDaBB6NtAECwvRtwcG4qJBdLdaAvR2d/9bSQV3/5q7/y9Jz6jh+46StHw3rP2Vzy33OEmPM7NvmNmNlenDQ5jZBWa218z23n///TW8NZBuMzMxkjEyEgEaaCB6NtBgi4sxY9jfH8vt2BkWtQToQuXXe83sl83syZKOruH7Vvvr5Sue90o6QdKpkl4p6W/M7JD/17n7Lnff6e47Dz/88BreGkivubkI0ENDrJ9DU9CzgQbK5yM89/XFLUYJz5BqWwP9x2Y2LulNinuJjkm6qIbv2y/pmGXPj5b0k1WOudHdC5J+bGZ3KJpzLdONQNuZn2cKEE1HzwYapFCIW4xmMoRnPNKGI9Du/nl3n3L3W939ee7+VEkP1fDaN0k6wcyON7N+Sa+QdM2KY66W9DxJMrMdiunBH9V1BkCbWFyM+4ZmszEFCDQDPRtojGIxwnNPT4Tnnlrm7NE11vzrYGYZM3ulmb3ZzE6qfO4sM7tB0vs3emF3L0q6UNKXJd0u6TPufpuZXWpmZ1cO+7KkB83s+5Kuk/S77v7gFs8JSJ2lpYPr57ZtYxQDjUfPBhqnVIrwLEV4zrAVEVZYbwnH3yqm874t6Qozu1vSMyVd7O5X1/Li7n6tpGtXfO7tyz52SW+sPICOlM/HRim9vYRnNBU9G2iAcjnCs3uE595aFrui66z312KnpCe5e9nMBiQ9IOnn3f2nrSkNaH+FQoTn6vo5pgDRRPRsYIuq4blUip7d15d0RUir9X6c5929LEnuvijpP2jEQO2q6+fMCM9oCXo2sAXuMeBRLMZsYX9/0hUhzdYbgf4FM7ul8rFJemzluSlm8p7U9OqANsX6OSSAng1sUjU85/NxkXc2m3RFSLv1AvQTWlYF0EGqU4DlsrRjB+vn0DL0bNdKBwUAACAASURBVGCTcrm42HtiIrbpBjay5o92d7+7lYUAncD94Pq5bdtYP4fWoWcDm5PLxTbdY2OxwRVQC1ZlAg1SnQIsFJgCBIB2MD0dG1yNjMQDqBUBGmiQAwdiCvCww2KnQQBAes3OxmN4OEafgXrUFKDNbNDMHt/sYoB2deBA7DQ4Ps76OSSPng2sb24uRp8HB6NvA/XaMECb2Ysl7ZP0pcrzSTNbub0r0LWmpmL93OhojGQASaJnA+tbWIi+PTAQFw0Cm1HLCPQ7JD1NUk6S3H2fpOOaVxLQPmZmYiRjZCQCNJAC7xA9G1jV4mLMGPb3x3I7dobFZtUSoIvuPtX0SoA2MzcXAXpoiPVzSBV6NrCKfD7Cc19f3J+f8IytqOUOtbea2XmSMmZ2gqQ3SLqhuWUB6TY/zxQgUoueDaxQKMQtRjMZwjMao5YR6NdLeqKkJUm7JU1JuqiZRQFptrgY9w3NZmMKEEgZejawTLEY4bmnJ8JzD/cfQwPUMgL9eHe/RNIlzS4GSLulpYPr57ZtYxQDqdR9PXvXLmn37oPP9+2TJieTqwepUSpFeJYiPGcyydaDzlHL/8PeY2Y/MLM/MrMnNr0iIKXy+dgopbeX8IxU676evXt3hOaqyUnpvPOSqwepUC5HeHaP8Nxby5AhUKMN/zq5+/PM7NGSXiZpl5mNSfq0u/9x06sDUqJQiPBcXT/HFCDSqmt79uSktGdP0lUgJarhuVSKnt3Xl3RF6DQ1xQB3/6m7XyHptxT3F317U6sCUqS6fs6M8Iz2QM9GN3OPAY9iMWYL+/uTrgidqJaNVJ5gZu8ws1slvV9xNffRTa8MSAHWz6Hd0LPRzarhOZ+POyRls0lXhE5Vy4qgv5P0SUkvdPefNLkeIDWqU4DlsrRjB+vn0Dbo2ehauVxc7D0xEdt0A81SyxroZ7SiECBNqqMYpVJMAbJ+Du2Cno1ulcvFNt1jY7HBFdBMawZoM/uMu7/MzP5dki//kiR39yc1vTogAcunALdtYwoQ7YGejW42PR0bXI2MxANotvVGoP+fyq9ntaIQIC0OHIgpwMMOi50GgTZBz0ZXmp2Nx/BwjD4DrbDmRYTufm/lw99297uXPyT9dmvKA1orl4udBsfHWT+H9kLPRjeam4vR58HB6NtAq9RyQ67TV/ncmY0uBEja1FRMAY6OxkgG0Kbo2egKCwvRtwcG4qJBoJXWWwP9OsWoxc+Z2S3LvjQq6RvNLgxopZmZGMkYGYkADbQbeja6ydJSLLfr74/lduwMi1Zbbw30bklflPROSRcv+/yMuz/U1KqAFpqbiwA9NMT6ObQ1eja6Qj4fF3r39cWF3oRnJGG9AO3ufpeZ/c7KL5jZNhoyOsH8PFOA6Bj0bHS8QiHuz5/JsDMskrXRCPRZkm5W3BJp+f/xXNLPNbEuoOkWF+OiwWw2pgCBNkfPRkcrFiM89/QQnpG8NQO0u59V+fX41pUDtMby9XNMAaIT0LPRyUqlCM9ShOdMJtl6gA3//2Zmzzaz4crHrzaz95jZsc0vDWiO6vq5TIbwjM5Dz0anKZcjPLtHeO7dcA9loPlqmQD5oKR5MztF0u9JulvSx5paFdAkxeLB8MwUIDoUPRsdoxqeS6UY8OjrS7oiINQSH4ru7pLOkfQ+d3+f4rZIQFupTgGaMQWIjkbPRkdwjwGPYjHCc39/0hUBB9UyETJjZm+R9H9Leo6ZZSTxf0C0lVJJeuCBaMg7dhCe0dHo2Wh71fCcz8dF3tls0hUBj1TLCPTLJS1J+l/u/lNJR0m6rKlVAQ1UnQIsl1k/h65Az0bby+XiYu+JidimG0ibDQN0pQF/QtK4mZ0ladHdP9r0yoAGqI5isH4O3YKejXY3NRXbdI+NxQZXQBrVcheOl0n6tqRflfQySd8ys3ObXRiwVUwBohvRs9HOpqdjd9iRkXgAaVXLZPYlkn7R3e+TJDM7XNJXJX22mYUBW3XgQEwBHnZY7DQIdAl6NtrS7Gw8hodj9BlIs1rWQPdUG3HFgzV+H5CYXC52GhwfZ/0cug49G21nfj5GnwcHo28DaVfLCPSXzOzLkj5Zef5ySdc2ryRga6amohmPjsZIBtBl6NloKwsLMegxMBAXDQLtYMMA7e6/a2b/l6T/Lskk7XL3q5peGbAJMzOxfm54OAI00G3o2WgnS0ux3K6/P5bbsTMs2sWaAdrMTpB0uaTHSvp3SW929/9qVWFAvebmIkAPDTEFiO5Dz0a7yefjQu++vrhLEuEZ7WS9dXEflvR5SS+VdLOkv2hJRcAmzM/H0g2mANHF6NloG4VC3J8/k4n78/ewSh9tZr0lHKPu/qHKx3eY2XdaURBQr8XFWD+XzcYUINCluqdn79ol7d598Pm+fdLkZHL1oC7FYoTnnh7CM9rXegF6wMyerFhDJ0mDy5+7e+c2Z7SN5evnmAJEl+uenr179yND8+SkdN55ydaEmpRKEZ6lCM+ZTLL1AJu1XoC+V9J7lj3/6bLnLun5zSoKqEV1/VwmQ3gG1G09e3JS2rMn6SpQh3I5wnO5LO3YIfXWch8wIKXW/Ovr7s9rZSFAPYrFg+GZKUCAno10q4bnUil6dl9f0hUBW0PsQNupTgGaMQUIAGnnHgMehULMFvb3J10RsHUEaLSVUkl64IFoyIRnAEg397hOJZ+Pi7yz2aQrAhqDAI22sXz93LZtrJ8DgLTL5eJOSRMTsU030Ck2DNAWXm1mb688P9bMntb80oCDqlOApRJTgMB66NlIi6mp2KZ7bCw2uAI6SS0j0H8p6ZmSXll5PiPpA7W8uJmdYWZ3mNmdZnbxOseda2ZuZjtreV10l2p4ZgoQqAk9G4mbno7dYUdG4gF0mloC9NPd/XckLUqSux+QtOH4n5llFE37TEknSnqlmZ24ynGjkt4g6Vt11I0ucuBA3O95YiJ2GgSwLno2EjU7G4/h4Rh9BjpRLQG6UGmsLklmdrikcg3f9zRJd7r7j9w9L+lTks5Z5bg/kvRuVZo9sFx1/dz4OFOAQI06r2fv2iWdeurBx759LXtr1Gd+PkafBwejbwOdqpYAfYWkqyQ9ysz+RNK/SfrTGr7vKEn3LHu+v/K5h1V2yTrG3T+/3guZ2QVmttfM9t5///01vDU6wdRUNOPR0RjJAFCTzuvZ1Z0Hq9h5MJUWFmLQI5uNGUOgk214HwN3/4SZ3SzpNMWWsC9x99treO3V9oXzh79o1iPpvZLOr6GGXZJ2SdLOnTt9g8PRAWZmYv3c8HAEaAC16diezc6Dqba0FOG5v5+dYdEdNgzQZnaspHlJ/7T8c+7+nxt8635Jxyx7frSknyx7PirpJEl7LP6lPVrSNWZ2trvvra18dKK5uQjQQ0NMAQL1omej1fL5uNC7t5fwjO5Ry510v6AYhTBJA5KOl3SHpCdu8H03STrBzI6X9F+SXiHp4Tk3d5+StKP63Mz2SHozjbi7zc/H0o2BAaYAgU2iZ6NlCoUIz5lMbG7Vw+4S6BK1LOE4eflzM3uKpN+s4fuKZnahpC9Lykj6sLvfZmaXStrr7tdssmZ0qMXFg+vnDjss6WqA9kTPRqsUi7G5lRnhGd2n7r3c3P07ZvaLNR57raRrV3zu7Wsce2q9taBzLC3F7epYPwc0Fj0bzVAqRXiWIjxnMsnWA7RaLWug37jsaY+kp0jiVhhomOr6uUyG8AxsFT0bzVYuR3gul6UdO2LtM9Btavlrv/weCEXF+rp/aE456DbFYoTnnh6mAIEGoWejaarhuVSKnt3Xl3RFQDLWDdCVm/GPuPvvtqgedJHqFKBZjGIwBQhsDT0bzeQeAx6FQswW9m+4vyXQudYc7zOzXncvKab/gIYqlaQHHoiGzPo5YOvo2Wgm97hOJZ+Pi7wHBpKuCEjWeiPQ31Y04n1mdo2kv5c0V/2iu3+uybWhQy1fP7d9O+vngAahZ6Npcrm4U9LERGzTDXS7WqLLNkkPSnq+Dt5b1CXRjFG36hRgqcQUINAk9Gw01NRUbNM9NhYbXAFYP0A/qnI196062ISr2E4bdauG53w+wnM2m3RFQEehZ6Phpqdjd9iRkXgACOsF6IykET2yCVfRjFG3Awfifs8TE6yfA5qAno2Gmp2Nx/BwjD4DOGi9AH2vu1/askrQ0arr58bHmQIEmoSejYaZn4/R58HB6NsAHmm9u+6ynQUaYmoqmvHoaIxkAGgKejYaYmEhBj2y2ZgxBHCo9QL0aS2rAh1rZibWzw0PR4AG0DT0bGzZ0lKE5/5+doYF1rNmgHb3h1pZCDrP3FwE6KEhpgCBZqNnY6vy+bjQu7eX8AxshI2T0RTz87F0Y2CAKUAASLtCIcJzJhP35+8hHQDr4p8IGm5x8eD6ucMOS7oaAMB6isXY3MqM8AzUin8maKilpbhdHevnACD9SqUIz1KE50wm2XqAdkGARsNU189lMoRnAEi7cjnCc7kc4bm3lr2JAUgiQKNBisUIzz09TAECQNpVw3OpFD27ry/pioD2QszBllWnAM2kHTuYAgSANHOPAY9CIa5T6e9PuiKg/RCgsSWlkvTAA9GQWT8HAOnmHtep5PMRngcGkq4IaE8EaGza8vVz27axfg4A0i6XizsljY/HNt0ANocAjU2pTgGWShGemQIEgHSbmoptusfGYndYAJtHgEbdquG5OgWYzSZdEQBgPdPTsTvsyEg8AGwNARp1O3Ag7vc8McH6OQBIu9nZeAwNxegzgK0jQKMuy9fPDQ0lXQ0AYD3z8zH6PDgYgx4AGoMAjZpNT0czHh1l/RwApN3CQgx6ZLOEZ6DRCNCoycxMTAEOD0eABgCk19JShOf+fnaGBZqBAI0Nzc1FgB4aiqUbAID0yufjQu/eXsIz0CwEaKxrYSFufTQwwBQgAKRdoRDhOZOJza16+CkPNAX/tLCmxcW440Y2G7erAwCkV7EYm1uZEZ6BZuOfF1a1tBThmfVzAJB+pVKEZynCcyaTbD1ApyNA4xDV9XOZDOEZANKuXI7wXC5HeO7tTboioPMRoPEIxWKE554epgABIO3cIzyXStGz+/qSrgjoDsQjPKw6BWgm7djBFCAApFk1PBcKcZ1Kf3/SFQHdgwANSQfDszvr5wAg7dzjOpV8PsLzwEDSFQHdhQANlcuxbKNUijXPrJ8DgHTL5eJOSePjsU03gNYiQHc59wjPxWKEZ6YAASDdpqbiHv1jY7E7LIDWI0B3sWp4rk4BZrNJVwQAWM/0dOwOOzISDwDJIEB3sQMH4n7PExOsnwOAtJudjcfQUIw+A0gOAbpLLV8/NzSUdDUAgPXMz8fo8+BgDHoASBYBugtNT0czHh1l/RwApN3CQgx6ZLOEZyAtCNBdZmYmpgCHhyNAAwDSa2kpwnN/PzvDAmlCgO4ic3MRoIeGYukGACC98vm40Lu3l/AMpA0BukssLMStjwYGmAIEgLQrFCI8ZzKxuVUPP62BVOGfZBdYXIw7bmSzcbs6AEB6FYuxM6wZ4RlIK/5ZdrilpQjPrJ8DgPQrlSI8SxGeM5lk6wGwOgJ0B1s+BUh4BoB0K5cjPJfLEZ57e5OuCMBaCNAdqjoF2NPDFCAApJ179OxSKXp2X1/SFQFYD7GqAzEFCADtoxqeC4W4TqW/P+mKAGyEAN1hquHZXdqxgylAAEgz97hOJZ+P8DwwkHRFAGpBgO4g5XKseS6VYs0z4RkA0i2XizsljY/HNt0A2kNTA7SZnWFmd5jZnWZ28Spff6OZfd/MbjGzfzGzxzSznk7mHuG5WIzwzBQggHrRs1trairu0T82FrvDAmgfTQvQZpaR9AFJZ0o6UdIrzezEFYd9V9JOd3+SpM9Kenez6ulk1fBcnQLMZpOuCEC7oWe31sxM7A47MhIPAO2lmSPQT5N0p7v/yN3zkj4l6ZzlB7j7de4+X3l6o6Sjm1hPxzpwIO73PDHB+jkAm0bPbpHZ2QjQQ0Mx+gyg/TQzQB8l6Z5lz/dXPreW10r6YlMq2bVLOvXUeOzb15S3SMry9XNDQ0lXA6CNpadnd7D5eWl6OtY7T0wkXQ2AzWpmgF5t2w5f9UCzV0vaKemyNb5+gZntNbO9999/f/2V7N59MDhPTkrnnVf/a6TQ9HQ049FR1s8B2LL09OwOtbAQgx7ZLOEZaHfNvE/DfknHLHt+tKSfrDzIzF4g6RJJz3X3pdVeyN13SdolSTt37ly1oW9oclLas2dT35pGMzMxDTg8HAEaALYoXT27wywtRXju72dnWKATNHME+iZJJ5jZ8WbWL+kVkq5ZfoCZPVnSX0s6293va2ItHWVu7uD6ufHxpKsB0CHo2U2Sz8eF3r29hGegUzQtQLt7UdKFkr4s6XZJn3H328zsUjM7u3LYZZJGJP29me0zs2vWeDlULCzErY8GBpgCBNA49OzmKBQiPGcysTNsD7svAB2hqVttuPu1kq5d8bm3L/v4Bc18/06zuBh33Mhm43Z1ANBI9OzGKhZjZ1gzwjPQafjn3CaWliI8s34OANKvVIrwLEV4zmSSrQdAYxGg28DyKUDCMwCkW7kc4blcjvDc29S5XgBJIECnXHUKsKeHKUAASDv36NmlUvTsvr6kKwLQDMSxFGMKEADaRzU8FwpxnUp/f9IVAWgWAnRKVacA3aUdO5gCBIA0c4/rVPL5CM8DA0lXBKCZCNApVA3PpVKseSY8A0C65XJxp6Tx8dimG0BnI0CnjHtcMFgsRnhmChAA0m1qKu7RPzYWu8MC6HwE6BSphufqFGA2m3RFAID1zMzE7rAjI/EA0B0I0Cly4EDc73ligvVzAJB2s7MRoIeGYvQZQPcgQKfE8vVzQ0NJVwMAWM/8vDQ9HeudJyaSrgZAqxGgU2B6Oprx6Cjr5wAg7RYWYtAjmyU8A92KAJ2wmZmYBhwejgANAEivpaUIz/397AwLdDMCdILm5g6unxsfT7oaAMB68vm40Lu3l/AMdDvuMJyQhYW49dHAAFOAAPAIu3ZJu3cffL5vnzQ5mVw9it0FH3oodoTdvl3qYfgJ6Gq0gAQsLsYdN7LZuF0dAGCZ3bsjNFdNTkrnnZdYOcVibG5lRngGEBiBbrGlpQjPfX1MAQLAmiYnpT17kq5CpVKEZynCcyaTbD0A0oH/R7fQyilAwjMApFe5HOG5XI6e3cuQE4AKAnSLVKcAe3qYAgSAtHOPnl0qRc/u60u6IgBpQoxrAaYAAaB9VMNzoRDXqfT3J10RgLQhQDdZdQrQnSlAAEg797hOJZ+P8DwwkHRFANKIAN1E1fBcKsUFg0wBAkC65XJxp6Tx8dimGwBWQ4BuEve4YLBYjPDMFCAApNvUVNyjf2wsdocFgLUQoJugGp6rU4DZbNIVAQDWMzMTu8OOjMQDANZDgG6CXC7u9zwxwfo5AEi72dkI0ENDMfoMABshQDdYLndwCnBoKOlqAADrmZ+XpqdjvfPERNLVAGgXBOgGmp6OZjw6yhQgAKTdwkIMemSzhGcA9SFAN8jMTEwDDg9HgAYApNfSUoTn/v640JudYQHUgwDdAHNzEaAHB+PWRwCA9Mrn40Lv3l7CM4DNIUBv0cJC3PpoYIApQABIu0IhwnMmE5tb9fBTEMAm0Dq2YHExdqzKZuN2dYxiAEB6FYuxuZUZ4RnA1tA+Nimfj/Dc18cUIACkXakU4VmK8JzJJFsPgPZGgN6EQiEacXUKkPAMAOlVLkfPLpejZ/f2Jl0RgHZHgK5TdQqwp4cpQABIO/fo2aVS9Oy+vqQrAtAJiH91YAoQANqHe1wwWCjEdSr9/UlXBKBTEKBrVJ0CdGcKEADSzj2uU1laivA8MJB0RQA6CQG6BtXwXCrFBYNMAQJAuuVycaek8fG4Rz8ANBIBegPVKcBiMcIzU4AAkG5TU3GP/tHR2B0WABqNAL2OanjO52MKMJtNuiIAwHpmZmJ32JGRCNAA0AwE6HXkcrF+bmKC9XMAkHazsxGgh4aksbGkqwHQyQjQa8jlYgpwbCyaMQAgvebnpenpWO88MZF0NQA6HQF6FdPT0YxHR2MaEACQXouLMeiRzRKeAbQGAXqF2dl4DA+zfg4A0m5pKW5X198fF3qzMyyAViBALzM3d3AKcHw86WoAAOvJ5+NC795ewjOA1iJAVywsxK2PBgaYAgSAtCsUIjxnMrG5VQ8/zQC0EC1HsX7uwIFYP3fYYYxiAECaFYuxuZUZ4RlAMrq+7eTzEZ77+pgCBIC0K5UiPEsRnjOZZOsB0J26OkAXCtGIq1OAhGcASK9yOXp2uRw9u7c36YoAdKuuDdDVKcCeHqYAASDt3KNnl0oxW9jXl3RFALpZV8ZGpgABoH24xwWDhUJcp5LNJl0RgG7XdQG6OgXozhQgAKSde1ynsrQU4XlgIOmKAKDLAnQ1PDMFCADtIZeLOyWNj8c9+gEgDZoaoM3sDDO7w8zuNLOLV/l61sw+Xfn6t8zsuGbVUp0CLBYjPPf3N+udAKA9palnS3Fv/oWF2BV2eLiZ7wQA9WlagDazjKQPSDpT0omSXmlmJ6447LWSDrj7z0t6r6Q/a0Yt7tJDxTHl86yfA4DVpKlnS9JMcVBzc9LISARoAEiTZo5AP03Sne7+I3fPS/qUpHNWHHOOpCsrH39W0mlmjb+ZXK44oqVynyYmWD8HAGtITc+eLQ1qpjSkoSFpbKzRrw4AW9fMS+iOknTPsuf7JT19rWPcvWhmU5K2S3pg+UFmdoGkCyTp2GOPrbsQe9LJGrOShobq/lYA6Bap6tmDpYwmJur+VgBoiWYG6NVGJXwTx8jdd0naJUk7d+485OsbmfjgO+v9FgDoNqnp2cMfeLdY8gwgzZq5hGO/pGOWPT9a0k/WOsbMeiWNS3qoiTUBAFZHzwaAGjUzQN8k6QQzO97M+iW9QtI1K465RtJrKh+fK+lf3b3u0QoAwJbRswGgRk1bwlFZH3ehpC9Lykj6sLvfZmaXStrr7tdI+ltJHzOzOxWjGK9oVj0AgLXRswGgdk3dh8/dr5V07YrPvX3Zx4uSfrWZNQAAakPPBoDadNVOhAAAAMBWEaABAACAOhCgAQAAgDoQoAEAAIA6EKABAACAOhCgAQAAgDoQoAEAAIA6EKABAACAOhCgAQAAgDqYuyddQ13M7H5Jd2/iW3dIeqDB5aRFJ5+b1Nnnx7m1r82c32Pc/fBmFJNW9OxVdfK5SZ19fp18blJnn99mz23Vvt12AXqzzGyvu+9Muo5m6ORzkzr7/Di39tXp55e0Tv797eRzkzr7/Dr53KTOPr9GnxtLOAAAAIA6EKABAACAOnRTgN6VdAFN1MnnJnX2+XFu7avTzy9pnfz728nnJnX2+XXyuUmdfX4NPbeuWQMNAAAANEI3jUADAAAAW9ZxAdrMzjCzO8zsTjO7eJWvZ83s05Wvf8vMjmt9lZtTw7m90cy+b2a3mNm/mNljkqhzMzY6t2XHnWtmbmZtdZVwLednZi+r/PndZma7W13jZtXw9/JYM7vOzL5b+bv5oiTq3Awz+7CZ3Wdmt67xdTOzKyrnfouZPaXVNbY7enZ79myps/s2PZuevSF375iHpIyk/yPp5yT1S/qepBNXHPPbkv6q8vErJH066bobeG7PkzRU+fh1nXRuleNGJV0v6UZJO5Ouu8F/didI+q6kwyrPH5V03Q08t12SXlf5+ERJdyVddx3n90uSniLp1jW+/iJJX5Rkkp4h6VtJ19xOD3p2e/bsWs+vclzb9W16Nj27lkenjUA/TdKd7v4jd89L+pSkc1Ycc46kKysff1bSaWZmLaxxszY8N3e/zt3nK09vlHR0i2vcrFr+3CTpjyS9W9JiK4trgFrO7zckfcDdD0iSu9/X4ho3q5Zzc0ljlY/HJf2khfVtibtfL+mhdQ45R9JHPdwoacLMjmxNdR2Bnt2ePVvq7L5Nz6Znb6jTAvRRku5Z9nx/5XOrHuPuRUlTkra3pLqtqeXclnut4n9Z7WDDczOzJ0s6xt0/38rCGqSWP7vHSXqcmX3DzG40szNaVt3W1HJu75D0ajPbL+laSa9vTWktUe+/SzwSPfugdurZUmf3bXo2PXtDvQ0pJz1WG5VYeZuRWo5Jo5rrNrNXS9op6blNrahx1j03M+uR9F5J57eqoAar5c+uVzEleKpiFOrrZnaSu+eaXNtW1XJur5T0EXf/32b2TEkfq5xbufnlNV279pO0oGerLXu21Nl9m55Nz95Qp41A75d0zLLnR+vQqYeHjzGzXsX0xHrD/WlRy7nJzF4g6RJJZ7v7Uotq26qNzm1U0kmS9pjZXYp1S9e00QUptf69/Ed3L7j7jyXdoWjOaVfLub1W0mckyd2/KWlA0o6WVNd8Nf27xJro2e3Zs6XO7tv0bHr2hjotQN8k6QQzO97M+hUXnFyz4phrJL2m8vG5kv7VKyvLU27Dc6tMl/21ohG3y3osaYNzc/cpd9/h7se5+3GKtYJnu/veZMqtWy1/L69WXFAkM9uhmB78UUur3Jxazu0/JZ0mSWb2BEUzvr+lVTbPNZL+Z+XK7mdImnL3e5Muqo3Qs9uzZ0ud3bfp2fTsjSV5tWQzHoorLP9DcZXpJZXPXar4hyvFX4S/l3SnpG9L+rmka27guX1V0s8k7as8rkm65kad24pj96hNruau48/OJL1H0vcl/bukVyRdcwPP7URJ31Bc7b1P0guTrrmOc/ukpHslFRQjF6+V9FuSfmvZn9sHKuf+7+329zIND3p2e/bsWs5vxbFt1bfp2fTsjR7sRAgAAADUodOWcAAAAABNRYAGAAAA6kCABgAAAOpAgAYAAADqQIAGAAAA6kCARtOYWcnM9i17HLfOsbMNeL+PmNmPK+/1ncoOSvW+xt+Y2YmVj/+/FV+7Yas1Vl6n+vtyq5n9k5lNbHD8pJm9qBHvDQBroWev+R70bByC29ihacxs1t1HGn3sOq/xEUmfd/fPmtkLJV3u7k/awutt/YELHQAABB1JREFUuaaNXtfMrpT0H+7+J+scf77iXpUXNroWAKiiZ2/8uvRsVDECjZYxsxEz+5fKSMO/m9k5qxxzpJldv+x/+8+pfP6FZvbNyvf+vZlt1CSvl/Tzle99Y+W1bjWziyqfGzazL5jZ9yqff3nl83vMbKeZvUvSYKWOT1S+Nlv59dPLRxcqoygvNbOMmV1mZjeZ2S1m9ps1/LZ8U9JRldd5mpndYGbfrfz6+MpOUZdKenmllpdXav9w5X2+u9rvIwBsFT17VfRshKR3jeHRuQ9JJR3cYesqSb2Sxipf26HYWaw6CzJb+fVNOrgzUkbSaOXY6yUNVz7/+5Levsr7fUTSuZWPf1XStyQ9VbHb0LCkEUm3SXqypJdK+tCy7x2v/LpHlZ2JqjUtO6Za469IurLycb+keyQNSrpA0lsrn89K2ivp+FXqnF12fn8v6YzK8zFJvZWPXyDpHyofny/p/cu+/08lvbry8YRiR6nhpP+8efDg0d4PejY9m0ftj14BzbPg7pPVJ2bWJ+lPzeyXJJUV/4s/QtJPl33PTZI+XDn2anffZ2bPVWVrUTOTogF+c433vMzM3irpfsUWnqdJusrd5yo1fE7ScyR9SdLlZvZniinEr9dxXl+UdIWZZSWdIel6d1+oTEE+yczOrRw3LukEST9e8f2DZrZP0nGSbpb0lWXHX2lmJ0hySX1rvP8LJZ1tZm+uPB+QdKyk2+s4BwBYiZ5Nz0aNCNBopVdJOlzSU929YGZ3KRrJw9z9+kqz/mVJHzOzyyQdkPQVd39lDe/xu+7+2eoTM3vBage5+3+Y2VMlvUjSO83sn9390lpOwt0XzWyPpP8h6eWSPll9O0mvd/cvb/ASC+4+aWbjkj4v6XckXSHpjyRd5+6/YnHxzp41vt8kvdTd76ilXgDYJHp2oGfjEKyBRiuNS7qv0oifJ+kxKw8ws8dUjvmQpL+V9BRJN0p6tplV18cNmdnjanzP6yW9pPI9w4qpvK+b2X+TNO/uH5d0eeV9VipURlVW8ylJv6YYGak23y9Lel31e8zscZX3XJW7T0l6g6Q3V75nXNJ/Vb58/rJDZxTTolVflvR6qwztmNmT13oPANgCevYy9GwsR4BGK31C0k4z26sY2fjBKsecKmmfmX1Xsebtfe5+v6I5fdLMblE051+o5Q3d/TuKdXbfVqyv+xt3/66kkyV9uzItd4mkP17l23dJuqV6QcoK/yzplyR91d3zlc/9jaTvS/qOmd0q6a+1wSxPpZbvSXqFpHcrRla+oVhrV3WdpBOrF6QoRj36KrXdWnkOAI1Gzz60Pno2JHEbOwAAAKAujEADAAAAdSBAAwAAAHUgQAMAAAB1IEADAAAAdSBAAwAAAHUgQAMAAAB1IEADAAAAdSBAAwAAAHX4/wGN+AR7TGfd7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for gamma = 0.5 \n",
    "best_model.gamma = 0.5\n",
    "best_model.fit(X_train,y_train)\n",
    "\n",
    "plot_roc_test(best_model,X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for gamma = 50\n",
    "best_model.gamma = 50\n",
    "best_model.fit(X_train,y_train)\n",
    "\n",
    "plot_roc_test(best_model,X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can infer from the last two graphs that model with gamma = 50, is highly overfitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
